{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c9ec640",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch            : 1.10.1\n",
      "pytorch_lightning: 1.5.1\n",
      "torchmetrics     : 0.6.2\n",
      "matplotlib       : 3.3.4\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f85dcc1e",
   "metadata": {},
   "source": [
    "The three extensions below are optional, for more information, see\n",
    "- `watermark`:  https://github.com/rasbt/watermark\n",
    "- `pycodestyle_magic`: https://github.com/mattijn/pycodestyle_magic\n",
    "- `nb_black`: https://github.com/dnanhkhoa/nb_black"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c28bc5ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext pycodestyle_magic\n",
    "%flake8_on --ignore W291,W293,E703,E402,E999 --max_line_length=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "43d420d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"%load_ext nb_black\";\n",
       "                var nbb_formatted_code = \"%load_ext nb_black\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext nb_black"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "517d2bf4",
   "metadata": {},
   "source": [
    "<a href=\"https://pytorch.org\"><img src=\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\" width=\"90\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\"https://www.pytorchlightning.ai\"><img src=\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\" width=\"150\"/></a>\n",
    "\n",
    "# AlexNet Trained on CIFAR-10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d37a7a82",
   "metadata": {},
   "source": [
    "AlexNet [1][2] trained on CIFAR-10 [3].\n",
    "\n",
    "### References\n",
    "\n",
    "- [1] L13.7 CNN Architectures & AlexNet (20:17), https://www.youtube.com/watch?v=-IHxe4-09e4\n",
    "- [2] Imagenet classification with deep convolutional neural networks, https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf\n",
    "- [3] https://en.wikipedia.org/wiki/CIFAR-10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d9eeb2a",
   "metadata": {},
   "source": [
    "## General settings and hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37bb3dc1",
   "metadata": {},
   "source": [
    "- Here, we specify some general hyperparameter values and general settings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8350f2d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"BATCH_SIZE = 256\\nNUM_EPOCHS = 40\\nLEARNING_RATE = 0.0001\\nNUM_WORKERS = 4\";\n",
       "                var nbb_formatted_code = \"BATCH_SIZE = 256\\nNUM_EPOCHS = 40\\nLEARNING_RATE = 0.0001\\nNUM_WORKERS = 4\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BATCH_SIZE = 256\n",
    "NUM_EPOCHS = 40\n",
    "LEARNING_RATE = 0.0001\n",
    "NUM_WORKERS = 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6517b50d",
   "metadata": {},
   "source": [
    "- Note that using multiple workers can sometimes cause issues with too many open files in PyTorch for small datasets. If we have problems with the data loader later, try setting `NUM_WORKERS = 0` and reload the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7938d4cf",
   "metadata": {},
   "source": [
    "## Implementing a Neural Network using PyTorch Lightning's `LightningModule`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddb917fd",
   "metadata": {},
   "source": [
    "- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\n",
    "- In essence, `LightningModule` is a wrapper around a PyTorch module.\n",
    "- We start with defining our neural network model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "061ba942",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 5;\n",
       "                var nbb_unformatted_code = \"import torch.nn as nn\\nimport torch.nn.functional as F\\n\\n\\n# Regular PyTorch Module\\nclass PyTorchModel(nn.Module):\\n    def __init__(self, num_classes):\\n        super().__init__()\\n        self.features = nn.Sequential(\\n            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),\\n            nn.ReLU(inplace=True),\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\n            nn.Conv2d(64, 192, kernel_size=5, padding=2),\\n            nn.ReLU(inplace=True),\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\n            nn.Conv2d(192, 384, kernel_size=3, padding=1),\\n            nn.ReLU(inplace=True),\\n            nn.Conv2d(384, 256, kernel_size=3, padding=1),\\n            nn.ReLU(inplace=True),\\n            nn.Conv2d(256, 256, kernel_size=3, padding=1),\\n            nn.ReLU(inplace=True),\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\n        )\\n        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))\\n        self.classifier = nn.Sequential(\\n            nn.Dropout(0.5),\\n            nn.Linear(256 * 6 * 6, 4096),\\n            nn.ReLU(inplace=True),\\n            nn.Dropout(0.5),\\n            nn.Linear(4096, 4096),\\n            nn.ReLU(inplace=True),\\n            nn.Linear(4096, num_classes)\\n        )\\n\\n    def forward(self, x):\\n        x = self.features(x)\\n        x = self.avgpool(x)\\n        x = torch.flatten(x, start_dim=1)\\n        logits = self.classifier(x)\\n        return logits\";\n",
       "                var nbb_formatted_code = \"import torch.nn as nn\\nimport torch.nn.functional as F\\n\\n\\n# Regular PyTorch Module\\nclass PyTorchModel(nn.Module):\\n    def __init__(self, num_classes):\\n        super().__init__()\\n        self.features = nn.Sequential(\\n            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),\\n            nn.ReLU(inplace=True),\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\n            nn.Conv2d(64, 192, kernel_size=5, padding=2),\\n            nn.ReLU(inplace=True),\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\n            nn.Conv2d(192, 384, kernel_size=3, padding=1),\\n            nn.ReLU(inplace=True),\\n            nn.Conv2d(384, 256, kernel_size=3, padding=1),\\n            nn.ReLU(inplace=True),\\n            nn.Conv2d(256, 256, kernel_size=3, padding=1),\\n            nn.ReLU(inplace=True),\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\n        )\\n        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))\\n        self.classifier = nn.Sequential(\\n            nn.Dropout(0.5),\\n            nn.Linear(256 * 6 * 6, 4096),\\n            nn.ReLU(inplace=True),\\n            nn.Dropout(0.5),\\n            nn.Linear(4096, 4096),\\n            nn.ReLU(inplace=True),\\n            nn.Linear(4096, num_classes),\\n        )\\n\\n    def forward(self, x):\\n        x = self.features(x)\\n        x = self.avgpool(x)\\n        x = torch.flatten(x, start_dim=1)\\n        logits = self.classifier(x)\\n        return logits\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "# Regular PyTorch Module\n",
    "class PyTorchModel(nn.Module):\n",
    "    def __init__(self, num_classes):\n",
    "        super().__init__()\n",
    "        self.features = nn.Sequential(\n",
    "            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "            nn.Conv2d(64, 192, kernel_size=5, padding=2),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "            nn.Conv2d(192, 384, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(384, 256, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Conv2d(256, 256, kernel_size=3, padding=1),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.MaxPool2d(kernel_size=3, stride=2),\n",
    "        )\n",
    "        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))\n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Dropout(0.5),\n",
    "            nn.Linear(256 * 6 * 6, 4096),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Dropout(0.5),\n",
    "            nn.Linear(4096, 4096),\n",
    "            nn.ReLU(inplace=True),\n",
    "            nn.Linear(4096, num_classes)\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.features(x)\n",
    "        x = self.avgpool(x)\n",
    "        x = torch.flatten(x, start_dim=1)\n",
    "        logits = self.classifier(x)\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "449af085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\nimport pytorch_lightning as pl\\nimport torchmetrics\\n\\n\\n# LightningModule that receives a PyTorch model as input\\nclass LightningModel(pl.LightningModule):\\n    def __init__(self, model, learning_rate):\\n        super().__init__()\\n\\n        self.learning_rate = learning_rate\\n        # The inherited PyTorch module\\n        self.model = model\\n        if hasattr(model, \\\"dropout_proba\\\"):\\n            self.dropout_proba = model.dropout_proba\\n\\n        # Save settings and hyperparameters to the log directory\\n        # but skip the model parameters\\n        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\\n        # Set up attributes for computing the accuracy\\n        self.train_acc = torchmetrics.Accuracy()\\n        self.valid_acc = torchmetrics.Accuracy()\\n        self.test_acc = torchmetrics.Accuracy()\\n\\n    # Defining the forward method is only necessary\\n    # if you want to use a Trainer's .predict() method (optional)\\n    def forward(self, x):\\n        return self.model(x)\\n\\n    # A common forward step to compute the loss and labels\\n    # this is used for training, validation, and testing below\\n    def _shared_step(self, batch):\\n        features, true_labels = batch\\n        logits = self(features)\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n        predicted_labels = torch.argmax(logits, dim=1)\\n\\n        return loss, true_labels, predicted_labels\\n\\n    def training_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.log(\\\"train_loss\\\", loss)\\n\\n        # Do another forward pass in .eval() mode to compute accuracy\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\n        # during evaluation (inference)\\n        self.model.eval()\\n        with torch.no_grad():\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\n        self.train_acc(predicted_labels, true_labels)\\n        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n        self.model.train()\\n\\n        return loss  # this is passed to the optimzer for training\\n\\n    def validation_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.log(\\\"valid_loss\\\", loss)\\n        self.valid_acc(predicted_labels, true_labels)\\n        self.log(\\n            \\\"valid_acc\\\",\\n            self.valid_acc,\\n            on_epoch=True,\\n            on_step=False,\\n            prog_bar=True,\\n        )\\n\\n    def test_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.test_acc(predicted_labels, true_labels)\\n        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\\n    def configure_optimizers(self):\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n        return optimizer\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\nimport pytorch_lightning as pl\\nimport torchmetrics\\n\\n\\n# LightningModule that receives a PyTorch model as input\\nclass LightningModel(pl.LightningModule):\\n    def __init__(self, model, learning_rate):\\n        super().__init__()\\n\\n        self.learning_rate = learning_rate\\n        # The inherited PyTorch module\\n        self.model = model\\n        if hasattr(model, \\\"dropout_proba\\\"):\\n            self.dropout_proba = model.dropout_proba\\n\\n        # Save settings and hyperparameters to the log directory\\n        # but skip the model parameters\\n        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\\n        # Set up attributes for computing the accuracy\\n        self.train_acc = torchmetrics.Accuracy()\\n        self.valid_acc = torchmetrics.Accuracy()\\n        self.test_acc = torchmetrics.Accuracy()\\n\\n    # Defining the forward method is only necessary\\n    # if you want to use a Trainer's .predict() method (optional)\\n    def forward(self, x):\\n        return self.model(x)\\n\\n    # A common forward step to compute the loss and labels\\n    # this is used for training, validation, and testing below\\n    def _shared_step(self, batch):\\n        features, true_labels = batch\\n        logits = self(features)\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n        predicted_labels = torch.argmax(logits, dim=1)\\n\\n        return loss, true_labels, predicted_labels\\n\\n    def training_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.log(\\\"train_loss\\\", loss)\\n\\n        # Do another forward pass in .eval() mode to compute accuracy\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\n        # during evaluation (inference)\\n        self.model.eval()\\n        with torch.no_grad():\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\n        self.train_acc(predicted_labels, true_labels)\\n        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n        self.model.train()\\n\\n        return loss  # this is passed to the optimzer for training\\n\\n    def validation_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.log(\\\"valid_loss\\\", loss)\\n        self.valid_acc(predicted_labels, true_labels)\\n        self.log(\\n            \\\"valid_acc\\\",\\n            self.valid_acc,\\n            on_epoch=True,\\n            on_step=False,\\n            prog_bar=True,\\n        )\\n\\n    def test_step(self, batch, batch_idx):\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\n        self.test_acc(predicted_labels, true_labels)\\n        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\\n    def configure_optimizers(self):\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n        return optimizer\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\n",
    "import pytorch_lightning as pl\n",
    "import torchmetrics\n",
    "\n",
    "\n",
    "# LightningModule that receives a PyTorch model as input\n",
    "class LightningModel(pl.LightningModule):\n",
    "    def __init__(self, model, learning_rate):\n",
    "        super().__init__()\n",
    "\n",
    "        self.learning_rate = learning_rate\n",
    "        # The inherited PyTorch module\n",
    "        self.model = model\n",
    "        if hasattr(model, \"dropout_proba\"):\n",
    "            self.dropout_proba = model.dropout_proba\n",
    "\n",
    "        # Save settings and hyperparameters to the log directory\n",
    "        # but skip the model parameters\n",
    "        self.save_hyperparameters(ignore=[\"model\"])\n",
    "\n",
    "        # Set up attributes for computing the accuracy\n",
    "        self.train_acc = torchmetrics.Accuracy()\n",
    "        self.valid_acc = torchmetrics.Accuracy()\n",
    "        self.test_acc = torchmetrics.Accuracy()\n",
    "\n",
    "    # Defining the forward method is only necessary\n",
    "    # if you want to use a Trainer's .predict() method (optional)\n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "\n",
    "    # A common forward step to compute the loss and labels\n",
    "    # this is used for training, validation, and testing below\n",
    "    def _shared_step(self, batch):\n",
    "        features, true_labels = batch\n",
    "        logits = self(features)\n",
    "        loss = torch.nn.functional.cross_entropy(logits, true_labels)\n",
    "        predicted_labels = torch.argmax(logits, dim=1)\n",
    "\n",
    "        return loss, true_labels, predicted_labels\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.log(\"train_loss\", loss)\n",
    "\n",
    "        # Do another forward pass in .eval() mode to compute accuracy\n",
    "        # while accountingfor Dropout, BatchNorm etc. behavior\n",
    "        # during evaluation (inference)\n",
    "        self.model.eval()\n",
    "        with torch.no_grad():\n",
    "            _, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.train_acc(predicted_labels, true_labels)\n",
    "        self.log(\"train_acc\", self.train_acc, on_epoch=True, on_step=False)\n",
    "        self.model.train()\n",
    "\n",
    "        return loss  # this is passed to the optimzer for training\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.log(\"valid_loss\", loss)\n",
    "        self.valid_acc(predicted_labels, true_labels)\n",
    "        self.log(\n",
    "            \"valid_acc\",\n",
    "            self.valid_acc,\n",
    "            on_epoch=True,\n",
    "            on_step=False,\n",
    "            prog_bar=True,\n",
    "        )\n",
    "\n",
    "    def test_step(self, batch, batch_idx):\n",
    "        loss, true_labels, predicted_labels = self._shared_step(batch)\n",
    "        self.test_acc(predicted_labels, true_labels)\n",
    "        self.log(\"test_acc\", self.test_acc, on_epoch=True, on_step=False)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n",
    "        return optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "007135a4",
   "metadata": {},
   "source": [
    "## Setting up the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebdfebbb",
   "metadata": {},
   "source": [
    "- In this section, we are going to set up our dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db9e40b9",
   "metadata": {},
   "source": [
    "### Inspecting the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ed84f607",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "\n",
      "Training label distribution:\n",
      "\n",
      "Test label distribution:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(0, 1000),\n",
       " (1, 1000),\n",
       " (2, 1000),\n",
       " (3, 1000),\n",
       " (4, 1000),\n",
       " (5, 1000),\n",
       " (6, 1000),\n",
       " (7, 1000),\n",
       " (8, 1000),\n",
       " (9, 1000)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"# %load ../code_dataset/dataset_cifar10_check.py\\nfrom collections import Counter\\nfrom torchvision import datasets\\nfrom torchvision import transforms\\nfrom torch.utils.data import DataLoader\\n\\n\\ntrain_dataset = datasets.CIFAR10(\\n    root=\\\"./data\\\", train=True, transform=transforms.ToTensor(), download=True\\n)\\n\\ntrain_loader = DataLoader(\\n    dataset=train_dataset,\\n    batch_size=BATCH_SIZE,\\n    num_workers=NUM_WORKERS,\\n    drop_last=True,\\n    shuffle=True,\\n)\\n\\ntest_dataset = datasets.CIFAR10(\\n    root=\\\"./data\\\", train=False, transform=transforms.ToTensor()\\n)\\n\\ntest_loader = DataLoader(\\n    dataset=test_dataset,\\n    batch_size=BATCH_SIZE,\\n    num_workers=NUM_WORKERS,\\n    drop_last=False,\\n    shuffle=False,\\n)\\n\\ntrain_counter = Counter()\\nfor images, labels in train_loader:\\n    train_counter.update(labels.tolist())\\n\\ntest_counter = Counter()\\nfor images, labels in test_loader:\\n    test_counter.update(labels.tolist())\\n\\nprint(\\\"\\\\nTraining label distribution:\\\")\\nsorted(train_counter.items())\\n\\nprint(\\\"\\\\nTest label distribution:\\\")\\nsorted(test_counter.items())\";\n",
       "                var nbb_formatted_code = \"# %load ../code_dataset/dataset_cifar10_check.py\\nfrom collections import Counter\\nfrom torchvision import datasets\\nfrom torchvision import transforms\\nfrom torch.utils.data import DataLoader\\n\\n\\ntrain_dataset = datasets.CIFAR10(\\n    root=\\\"./data\\\", train=True, transform=transforms.ToTensor(), download=True\\n)\\n\\ntrain_loader = DataLoader(\\n    dataset=train_dataset,\\n    batch_size=BATCH_SIZE,\\n    num_workers=NUM_WORKERS,\\n    drop_last=True,\\n    shuffle=True,\\n)\\n\\ntest_dataset = datasets.CIFAR10(\\n    root=\\\"./data\\\", train=False, transform=transforms.ToTensor()\\n)\\n\\ntest_loader = DataLoader(\\n    dataset=test_dataset,\\n    batch_size=BATCH_SIZE,\\n    num_workers=NUM_WORKERS,\\n    drop_last=False,\\n    shuffle=False,\\n)\\n\\ntrain_counter = Counter()\\nfor images, labels in train_loader:\\n    train_counter.update(labels.tolist())\\n\\ntest_counter = Counter()\\nfor images, labels in test_loader:\\n    test_counter.update(labels.tolist())\\n\\nprint(\\\"\\\\nTraining label distribution:\\\")\\nsorted(train_counter.items())\\n\\nprint(\\\"\\\\nTest label distribution:\\\")\\nsorted(test_counter.items())\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_dataset/dataset_cifar10_check.py\n",
    "from collections import Counter\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "\n",
    "train_dataset = datasets.CIFAR10(\n",
    "    root=\"./data\", train=True, transform=transforms.ToTensor(), download=True\n",
    ")\n",
    "\n",
    "train_loader = DataLoader(\n",
    "    dataset=train_dataset,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    num_workers=NUM_WORKERS,\n",
    "    drop_last=True,\n",
    "    shuffle=True,\n",
    ")\n",
    "\n",
    "test_dataset = datasets.CIFAR10(\n",
    "    root=\"./data\", train=False, transform=transforms.ToTensor()\n",
    ")\n",
    "\n",
    "test_loader = DataLoader(\n",
    "    dataset=test_dataset,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    num_workers=NUM_WORKERS,\n",
    "    drop_last=False,\n",
    "    shuffle=False,\n",
    ")\n",
    "\n",
    "train_counter = Counter()\n",
    "for images, labels in train_loader:\n",
    "    train_counter.update(labels.tolist())\n",
    "\n",
    "test_counter = Counter()\n",
    "for images, labels in test_loader:\n",
    "    test_counter.update(labels.tolist())\n",
    "\n",
    "print(\"\\nTraining label distribution:\")\n",
    "sorted(train_counter.items())\n",
    "\n",
    "print(\"\\nTest label distribution:\")\n",
    "sorted(test_counter.items())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b2403d4",
   "metadata": {},
   "source": [
    "### Performance baseline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c800bfc",
   "metadata": {},
   "source": [
    "- Especially for imbalanced datasets, it's pretty helpful to compute a performance baseline.\n",
    "- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- we want our model to be better than that!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5d6f9749",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Majority class: 3\n",
      "Accuracy when always predicting the majority class:\n",
      "0.10 (10.00%)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 8;\n",
       "                var nbb_unformatted_code = \"# %load ../code_dataset/performance_baseline.py\\nmajority_class = test_counter.most_common(1)[0]\\nprint(\\\"Majority class:\\\", majority_class[0])\\n\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\nprint(\\\"Accuracy when always predicting the majority class:\\\")\\nprint(f\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\")\";\n",
       "                var nbb_formatted_code = \"# %load ../code_dataset/performance_baseline.py\\nmajority_class = test_counter.most_common(1)[0]\\nprint(\\\"Majority class:\\\", majority_class[0])\\n\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\nprint(\\\"Accuracy when always predicting the majority class:\\\")\\nprint(f\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_dataset/performance_baseline.py\n",
    "majority_class = test_counter.most_common(1)[0]\n",
    "print(\"Majority class:\", majority_class[0])\n",
    "\n",
    "baseline_acc = majority_class[1] / sum(test_counter.values())\n",
    "print(\"Accuracy when always predicting the majority class:\")\n",
    "print(f\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "350d3627",
   "metadata": {},
   "source": [
    "## A quick visual check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9261f0a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"# %load ../code_dataset/plot_visual-check_basic.py\\n%matplotlib inline\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\nimport torchvision\\n\\n\\nfor images, labels in train_loader:  \\n    break\\n\\nplt.figure(figsize=(8, 8))\\nplt.axis(\\\"off\\\")\\nplt.title(\\\"Training images\\\")\\nplt.imshow(np.transpose(torchvision.utils.make_grid(\\n    images[:64], \\n    padding=2,\\n    normalize=True),\\n    (1, 2, 0)))\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# %load ../code_dataset/plot_visual-check_basic.py\\n%matplotlib inline\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\nimport torchvision\\n\\n\\nfor images, labels in train_loader:\\n    break\\n\\nplt.figure(figsize=(8, 8))\\nplt.axis(\\\"off\\\")\\nplt.title(\\\"Training images\\\")\\nplt.imshow(\\n    np.transpose(\\n        torchvision.utils.make_grid(images[:64], padding=2, normalize=True), (1, 2, 0)\\n    )\\n)\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_dataset/plot_visual-check_basic.py\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torchvision\n",
    "\n",
    "\n",
    "for images, labels in train_loader:  \n",
    "    break\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.axis(\"off\")\n",
    "plt.title(\"Training images\")\n",
    "plt.imshow(np.transpose(torchvision.utils.make_grid(\n",
    "    images[:64], \n",
    "    padding=2,\n",
    "    normalize=True),\n",
    "    (1, 2, 0)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0141679a",
   "metadata": {},
   "source": [
    "### Setting up a `DataModule`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a91eb5d9",
   "metadata": {},
   "source": [
    "- There are three main ways we can prepare the dataset for Lightning. We can\n",
    "  1. make the dataset part of the model;\n",
    "  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the following subsection;\n",
    "  3. create a LightningDataModule.\n",
    "- Here, we will use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods, as we can see below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "15dd7261",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"import os\\n\\nfrom torch.utils.data.dataset import random_split\\nfrom torch.utils.data import DataLoader\\nfrom torchvision import transforms\\n\\n\\nclass DataModule(pl.LightningDataModule):\\n    def __init__(self, data_path=\\\"./\\\"):\\n        super().__init__()\\n        self.data_path = data_path\\n\\n    def prepare_data(self):\\n        datasets.CIFAR10(root=self.data_path, download=True)\\n\\n        self.train_transform = transforms.Compose(\\n            [\\n                transforms.Resize((70, 70)),\\n                transforms.RandomCrop((64, 64)),\\n                transforms.ToTensor(),\\n            ]\\n        )\\n\\n        self.test_transform = transforms.Compose(\\n            [\\n                transforms.Resize((70, 70)),\\n                transforms.CenterCrop((64, 64)),\\n                transforms.ToTensor(),\\n            ]\\n        )\\n        return\\n\\n    def setup(self, stage=None):\\n        train = datasets.CIFAR10(\\n            root=self.data_path,\\n            train=True,\\n            transform=self.train_transform,\\n            download=False,\\n        )\\n\\n        self.test = datasets.CIFAR10(\\n            root=self.data_path,\\n            train=False,\\n            transform=self.test_transform,\\n            download=False,\\n        )\\n\\n        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\n\\n    def train_dataloader(self):\\n        train_loader = DataLoader(\\n            dataset=self.train,\\n            batch_size=BATCH_SIZE,\\n            drop_last=True,\\n            shuffle=True,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return train_loader\\n\\n    def val_dataloader(self):\\n        valid_loader = DataLoader(\\n            dataset=self.valid,\\n            batch_size=BATCH_SIZE,\\n            drop_last=False,\\n            shuffle=False,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return valid_loader\\n\\n    def test_dataloader(self):\\n        test_loader = DataLoader(\\n            dataset=self.test,\\n            batch_size=BATCH_SIZE,\\n            drop_last=False,\\n            shuffle=False,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return test_loader\";\n",
       "                var nbb_formatted_code = \"import os\\n\\nfrom torch.utils.data.dataset import random_split\\nfrom torch.utils.data import DataLoader\\nfrom torchvision import transforms\\n\\n\\nclass DataModule(pl.LightningDataModule):\\n    def __init__(self, data_path=\\\"./\\\"):\\n        super().__init__()\\n        self.data_path = data_path\\n\\n    def prepare_data(self):\\n        datasets.CIFAR10(root=self.data_path, download=True)\\n\\n        self.train_transform = transforms.Compose(\\n            [\\n                transforms.Resize((70, 70)),\\n                transforms.RandomCrop((64, 64)),\\n                transforms.ToTensor(),\\n            ]\\n        )\\n\\n        self.test_transform = transforms.Compose(\\n            [\\n                transforms.Resize((70, 70)),\\n                transforms.CenterCrop((64, 64)),\\n                transforms.ToTensor(),\\n            ]\\n        )\\n        return\\n\\n    def setup(self, stage=None):\\n        train = datasets.CIFAR10(\\n            root=self.data_path,\\n            train=True,\\n            transform=self.train_transform,\\n            download=False,\\n        )\\n\\n        self.test = datasets.CIFAR10(\\n            root=self.data_path,\\n            train=False,\\n            transform=self.test_transform,\\n            download=False,\\n        )\\n\\n        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\n\\n    def train_dataloader(self):\\n        train_loader = DataLoader(\\n            dataset=self.train,\\n            batch_size=BATCH_SIZE,\\n            drop_last=True,\\n            shuffle=True,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return train_loader\\n\\n    def val_dataloader(self):\\n        valid_loader = DataLoader(\\n            dataset=self.valid,\\n            batch_size=BATCH_SIZE,\\n            drop_last=False,\\n            shuffle=False,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return valid_loader\\n\\n    def test_dataloader(self):\\n        test_loader = DataLoader(\\n            dataset=self.test,\\n            batch_size=BATCH_SIZE,\\n            drop_last=False,\\n            shuffle=False,\\n            num_workers=NUM_WORKERS,\\n        )\\n        return test_loader\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "from torch.utils.data.dataset import random_split\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision import transforms\n",
    "\n",
    "\n",
    "class DataModule(pl.LightningDataModule):\n",
    "    def __init__(self, data_path=\"./\"):\n",
    "        super().__init__()\n",
    "        self.data_path = data_path\n",
    "\n",
    "    def prepare_data(self):\n",
    "        datasets.CIFAR10(root=self.data_path, download=True)\n",
    "\n",
    "        self.train_transform = transforms.Compose(\n",
    "            [\n",
    "                transforms.Resize((70, 70)),\n",
    "                transforms.RandomCrop((64, 64)),\n",
    "                transforms.ToTensor(),\n",
    "            ]\n",
    "        )\n",
    "\n",
    "        self.test_transform = transforms.Compose(\n",
    "            [\n",
    "                transforms.Resize((70, 70)),\n",
    "                transforms.CenterCrop((64, 64)),\n",
    "                transforms.ToTensor(),\n",
    "            ]\n",
    "        )\n",
    "        return\n",
    "\n",
    "    def setup(self, stage=None):\n",
    "        train = datasets.CIFAR10(\n",
    "            root=self.data_path,\n",
    "            train=True,\n",
    "            transform=self.train_transform,\n",
    "            download=False,\n",
    "        )\n",
    "\n",
    "        self.test = datasets.CIFAR10(\n",
    "            root=self.data_path,\n",
    "            train=False,\n",
    "            transform=self.test_transform,\n",
    "            download=False,\n",
    "        )\n",
    "\n",
    "        self.train, self.valid = random_split(train, lengths=[45000, 5000])\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        train_loader = DataLoader(\n",
    "            dataset=self.train,\n",
    "            batch_size=BATCH_SIZE,\n",
    "            drop_last=True,\n",
    "            shuffle=True,\n",
    "            num_workers=NUM_WORKERS,\n",
    "        )\n",
    "        return train_loader\n",
    "\n",
    "    def val_dataloader(self):\n",
    "        valid_loader = DataLoader(\n",
    "            dataset=self.valid,\n",
    "            batch_size=BATCH_SIZE,\n",
    "            drop_last=False,\n",
    "            shuffle=False,\n",
    "            num_workers=NUM_WORKERS,\n",
    "        )\n",
    "        return valid_loader\n",
    "\n",
    "    def test_dataloader(self):\n",
    "        test_loader = DataLoader(\n",
    "            dataset=self.test,\n",
    "            batch_size=BATCH_SIZE,\n",
    "            drop_last=False,\n",
    "            shuffle=False,\n",
    "            num_workers=NUM_WORKERS,\n",
    "        )\n",
    "        return test_loader"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b10de24",
   "metadata": {},
   "source": [
    "- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the dataset loading -- if we run our code in a distributed setting, this will be called on each node / GPU. \n",
    "- Next, let's initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "31f5d214",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 11;\n",
       "                var nbb_unformatted_code = \"import torch\\n\\n\\ntorch.manual_seed(1) \\ndata_module = DataModule(data_path='./data')\";\n",
       "                var nbb_formatted_code = \"import torch\\n\\n\\ntorch.manual_seed(1)\\ndata_module = DataModule(data_path=\\\"./data\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "\n",
    "torch.manual_seed(1) \n",
    "data_module = DataModule(data_path='./data')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e188e446",
   "metadata": {},
   "source": [
    "## Training the model using the PyTorch Lightning Trainer class"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b671155",
   "metadata": {},
   "source": [
    "- Next, we initialize our model.\n",
    "- Also, we define a call back to obtain the model with the best validation set performance after training.\n",
    "- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. However, here, we will keep things simple and use the `CSVLogger`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c233056a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 12;\n",
       "                var nbb_unformatted_code = \"pytorch_model = PyTorchModel(num_classes=10)\";\n",
       "                var nbb_formatted_code = \"pytorch_model = PyTorchModel(num_classes=10)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pytorch_model = PyTorchModel(num_classes=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5d908c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 13;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\nfrom pytorch_lightning.loggers import CSVLogger\\n\\n\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\n\\ncallbacks = [\\n    ModelCheckpoint(\\n        save_top_k=1, mode=\\\"max\\\", monitor=\\\"valid_acc\\\"\\n    )  # save top 1 model\\n]\\nlogger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\nfrom pytorch_lightning.loggers import CSVLogger\\n\\n\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\n\\ncallbacks = [\\n    ModelCheckpoint(save_top_k=1, mode=\\\"max\\\", monitor=\\\"valid_acc\\\")  # save top 1 model\\n]\\nlogger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/logger_csv_acc_basic.py\n",
    "from pytorch_lightning.callbacks import ModelCheckpoint\n",
    "from pytorch_lightning.loggers import CSVLogger\n",
    "\n",
    "\n",
    "lightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\n",
    "\n",
    "callbacks = [\n",
    "    ModelCheckpoint(\n",
    "        save_top_k=1, mode=\"max\", monitor=\"valid_acc\"\n",
    "    )  # save top 1 model\n",
    "]\n",
    "logger = CSVLogger(save_dir=\"logs/\", name=\"my-model\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "930e5070",
   "metadata": {},
   "source": [
    "- Now it's time to train our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "87a0d60b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\n",
      "  rank_zero_deprecation(\n",
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name      | Type         | Params\n",
      "-------------------------------------------\n",
      "0 | model     | PyTorchModel | 57.0 M\n",
      "1 | train_acc | Accuracy     | 0     \n",
      "2 | valid_acc | Accuracy     | 0     \n",
      "3 | test_acc  | Accuracy     | 0     \n",
      "-------------------------------------------\n",
      "57.0 M    Trainable params\n",
      "0         Non-trainable params\n",
      "57.0 M    Total params\n",
      "228.179   Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b78391602428499884263ff7f9ba101e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation sanity check: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f20f98b6fd84ce4ada7a87e867278f0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validating: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training took 10.91 min in total.\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 14;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/trainer_nb_basic.py\\nimport time\\n\\n\\ntrainer = pl.Trainer(\\n    max_epochs=NUM_EPOCHS,\\n    callbacks=callbacks,\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\n    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n    logger=logger,\\n    deterministic=False,\\n    log_every_n_steps=10,\\n)\\n\\nstart_time = time.time()\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\n\\nruntime = (time.time() - start_time) / 60\\nprint(f\\\"Training took {runtime:.2f} min in total.\\\")\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/trainer_nb_basic.py\\nimport time\\n\\n\\ntrainer = pl.Trainer(\\n    max_epochs=NUM_EPOCHS,\\n    callbacks=callbacks,\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\n    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n    logger=logger,\\n    deterministic=False,\\n    log_every_n_steps=10,\\n)\\n\\nstart_time = time.time()\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\n\\nruntime = (time.time() - start_time) / 60\\nprint(f\\\"Training took {runtime:.2f} min in total.\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/trainer_nb_basic.py\n",
    "import time\n",
    "\n",
    "\n",
    "trainer = pl.Trainer(\n",
    "    max_epochs=NUM_EPOCHS,\n",
    "    callbacks=callbacks,\n",
    "    progress_bar_refresh_rate=50,  # recommended for notebooks\n",
    "    accelerator=\"auto\",  # Uses GPUs or TPUs if available\n",
    "    devices=\"auto\",  # Uses all available GPUs/TPUs if applicable\n",
    "    logger=logger,\n",
    "    deterministic=False,\n",
    "    log_every_n_steps=10,\n",
    ")\n",
    "\n",
    "start_time = time.time()\n",
    "trainer.fit(model=lightning_model, datamodule=data_module)\n",
    "\n",
    "runtime = (time.time() - start_time) / 60\n",
    "print(f\"Training took {runtime:.2f} min in total.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "261901c0",
   "metadata": {},
   "source": [
    "## Evaluating the model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75d7903f",
   "metadata": {},
   "source": [
    "- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (PS: you may want to check out [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) later on, which take care of it for us):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "29ddfdbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 15;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\n\\n\\nmetrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\\naggreg_metrics = []\\nagg_col = \\\"epoch\\\"\\nfor i, dfg in metrics.groupby(agg_col):\\n    agg = dict(dfg.mean())\\n    agg[agg_col] = i\\n    aggreg_metrics.append(agg)\\n\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\ndf_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"Loss\\\"\\n)\\ndf_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"ACC\\\"\\n)\\n\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\n\\n\\nmetrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\\naggreg_metrics = []\\nagg_col = \\\"epoch\\\"\\nfor i, dfg in metrics.groupby(agg_col):\\n    agg = dict(dfg.mean())\\n    agg[agg_col] = i\\n    aggreg_metrics.append(agg)\\n\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\ndf_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"Loss\\\"\\n)\\ndf_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"ACC\\\"\\n)\\n\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/logger_csv_plot_basic.py\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "metrics = pd.read_csv(f\"{trainer.logger.log_dir}/metrics.csv\")\n",
    "\n",
    "aggreg_metrics = []\n",
    "agg_col = \"epoch\"\n",
    "for i, dfg in metrics.groupby(agg_col):\n",
    "    agg = dict(dfg.mean())\n",
    "    agg[agg_col] = i\n",
    "    aggreg_metrics.append(agg)\n",
    "\n",
    "df_metrics = pd.DataFrame(aggreg_metrics)\n",
    "df_metrics[[\"train_loss\", \"valid_loss\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"Loss\"\n",
    ")\n",
    "df_metrics[[\"train_acc\", \"valid_acc\"]].plot(\n",
    "    grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"ACC\"\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5dd507e",
   "metadata": {},
   "source": [
    "- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cd2de3ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Restoring states from the checkpoint path at logs/my-model/version_18/checkpoints/epoch=35-step=6299.ckpt\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "Loaded model weights from checkpoint at logs/my-model/version_18/checkpoints/epoch=35-step=6299.ckpt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9ded09b2c8c14186894a4e3773d23207",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "DATALOADER:0 TEST RESULTS\n",
      "{'test_acc': 0.7247999906539917}\n",
      "--------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'test_acc': 0.7247999906539917}]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 16;\n",
       "                var nbb_unformatted_code = \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\";\n",
       "                var nbb_formatted_code = \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path=\\\"best\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8b2bc82",
   "metadata": {},
   "source": [
    "## Predicting labels of new data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35dddec4",
   "metadata": {},
   "source": [
    "- We can use the `trainer.predict` method either on a new `DataLoader` (`trainer.predict(dataloaders=...)`) or `DataModule` (`trainer.predict(datamodule=...)`) to apply the model to new data.\n",
    "- Alternatively, we can also manually load the best model from a checkpoint as shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2ba699c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logs/my-model/version_18/checkpoints/epoch=35-step=6299.ckpt\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 17;\n",
       "                var nbb_unformatted_code = \"path = trainer.checkpoint_callback.best_model_path\\nprint(path)\";\n",
       "                var nbb_formatted_code = \"path = trainer.checkpoint_callback.best_model_path\\nprint(path)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "path = trainer.checkpoint_callback.best_model_path\n",
    "print(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2e43e04a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 18;\n",
       "                var nbb_unformatted_code = \"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\nlightning_model.eval();\";\n",
       "                var nbb_formatted_code = \"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\nlightning_model.eval()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\n",
    "lightning_model.eval();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e8e78f",
   "metadata": {},
   "source": [
    "- For simplicity, we reused our existing `pytorch_model` above. However, we could also reinitialize the `pytorch_model`, and the `.load_from_checkpoint` method would load the corresponding model weights for us from the checkpoint file.\n",
    "- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "68c432d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([5, 5, 8, 0, 8])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 19;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/datamodule_testloader.py\\ntest_dataloader = data_module.test_dataloader()\\nacc = torchmetrics.Accuracy()\\n\\nfor batch in test_dataloader:\\n    features, true_labels = batch\\n\\n    with torch.no_grad():\\n        logits = lightning_model(features)\\n\\n    predicted_labels = torch.argmax(logits, dim=1)\\n    acc(predicted_labels, true_labels)\\n\\npredicted_labels[:5]\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/datamodule_testloader.py\\ntest_dataloader = data_module.test_dataloader()\\nacc = torchmetrics.Accuracy()\\n\\nfor batch in test_dataloader:\\n    features, true_labels = batch\\n\\n    with torch.no_grad():\\n        logits = lightning_model(features)\\n\\n    predicted_labels = torch.argmax(logits, dim=1)\\n    acc(predicted_labels, true_labels)\\n\\npredicted_labels[:5]\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/datamodule_testloader.py\n",
    "test_dataloader = data_module.test_dataloader()\n",
    "acc = torchmetrics.Accuracy()\n",
    "\n",
    "for batch in test_dataloader:\n",
    "    features, true_labels = batch\n",
    "\n",
    "    with torch.no_grad():\n",
    "        logits = lightning_model(features)\n",
    "\n",
    "    predicted_labels = torch.argmax(logits, dim=1)\n",
    "    acc(predicted_labels, true_labels)\n",
    "\n",
    "predicted_labels[:5]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e95cf2d6",
   "metadata": {},
   "source": [
    "- As an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d942de45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.7248 (72.48%)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 20;\n",
       "                var nbb_unformatted_code = \"test_acc = acc.compute()\\nprint(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\";\n",
       "                var nbb_formatted_code = \"test_acc = acc.compute()\\nprint(f\\\"Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_acc = acc.compute()\n",
    "print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82d33651",
   "metadata": {},
   "source": [
    "## Inspecting Failure Cases"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67e59adf",
   "metadata": {},
   "source": [
    "- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\n",
    "- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0039aede",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 21;\n",
       "                var nbb_unformatted_code = \"class_dict = {0: 'airplane',\\n              1: 'automobile',\\n              2: 'bird',\\n              3: 'cat',\\n              4: 'deer',\\n              5: 'dog',\\n              6: 'frog',\\n              7: 'horse',\\n              8: 'ship',\\n              9: 'truck'}\";\n",
       "                var nbb_formatted_code = \"class_dict = {\\n    0: \\\"airplane\\\",\\n    1: \\\"automobile\\\",\\n    2: \\\"bird\\\",\\n    3: \\\"cat\\\",\\n    4: \\\"deer\\\",\\n    5: \\\"dog\\\",\\n    6: \\\"frog\\\",\\n    7: \\\"horse\\\",\\n    8: \\\"ship\\\",\\n    9: \\\"truck\\\",\\n}\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_dict = {0: 'airplane',\n",
    "              1: 'automobile',\n",
    "              2: 'bird',\n",
    "              3: 'cat',\n",
    "              4: 'deer',\n",
    "              5: 'dog',\n",
    "              6: 'frog',\n",
    "              7: 'horse',\n",
    "              8: 'ship',\n",
    "              9: 'truck'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cc4cefe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 22;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/plot_failurecases_basic.py\\n# Append the folder that contains the\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n# files so we can import from them\\n\\nimport sys\\n\\nsys.path.append(\\\"../../pytorch_ipynb\\\")\\n\\nfrom helper_plotting import show_examples\\n\\n\\nshow_examples(\\n    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\n)\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/plot_failurecases_basic.py\\n# Append the folder that contains the\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n# files so we can import from them\\n\\nimport sys\\n\\nsys.path.append(\\\"../../pytorch_ipynb\\\")\\n\\nfrom helper_plotting import show_examples\\n\\n\\nshow_examples(model=lightning_model, data_loader=test_dataloader, class_dict=class_dict)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/plot_failurecases_basic.py\n",
    "# Append the folder that contains the\n",
    "# helper_data.py, helper_plotting.py, and helper_evaluate.py\n",
    "# files so we can import from them\n",
    "\n",
    "import sys\n",
    "\n",
    "sys.path.append(\"../../pytorch_ipynb\")\n",
    "\n",
    "from helper_plotting import show_examples\n",
    "\n",
    "\n",
    "show_examples(\n",
    "    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ae6820c",
   "metadata": {},
   "source": [
    "- In addition to inspecting failure cases visually, it is also informative to look at which classes the model confuses the most via a confusion matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "66f56407",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 23;\n",
       "                var nbb_unformatted_code = \"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\nfrom torchmetrics import ConfusionMatrix\\nimport matplotlib\\nfrom mlxtend.plotting import plot_confusion_matrix\\n\\n\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\n\\nfor x, y in test_dataloader:\\n\\n    with torch.no_grad():\\n        pred = lightning_model(x)\\n    cmat(pred, y)\\n\\ncmat_tensor = cmat.compute()\\ncmat = cmat_tensor.numpy()\\n\\nfig, ax = plot_confusion_matrix(\\n    conf_mat=cmat,\\n    class_names=class_dict.values(),\\n    norm_colormap=matplotlib.colors.LogNorm()  \\n    # normed colormaps highlight the off-diagonals \\n    # for high-accuracy models better\\n)\\n\\nplt.show()\";\n",
       "                var nbb_formatted_code = \"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\nfrom torchmetrics import ConfusionMatrix\\nimport matplotlib\\nfrom mlxtend.plotting import plot_confusion_matrix\\n\\n\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\n\\nfor x, y in test_dataloader:\\n\\n    with torch.no_grad():\\n        pred = lightning_model(x)\\n    cmat(pred, y)\\n\\ncmat_tensor = cmat.compute()\\ncmat = cmat_tensor.numpy()\\n\\nfig, ax = plot_confusion_matrix(\\n    conf_mat=cmat,\\n    class_names=class_dict.values(),\\n    norm_colormap=matplotlib.colors.LogNorm()\\n    # normed colormaps highlight the off-diagonals\\n    # for high-accuracy models better\\n)\\n\\nplt.show()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\n",
    "from torchmetrics import ConfusionMatrix\n",
    "import matplotlib\n",
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "\n",
    "\n",
    "cmat = ConfusionMatrix(num_classes=len(class_dict))\n",
    "\n",
    "for x, y in test_dataloader:\n",
    "\n",
    "    with torch.no_grad():\n",
    "        pred = lightning_model(x)\n",
    "    cmat(pred, y)\n",
    "\n",
    "cmat_tensor = cmat.compute()\n",
    "cmat = cmat_tensor.numpy()\n",
    "\n",
    "fig, ax = plot_confusion_matrix(\n",
    "    conf_mat=cmat,\n",
    "    class_names=class_dict.values(),\n",
    "    norm_colormap=matplotlib.colors.LogNorm()  \n",
    "    # normed colormaps highlight the off-diagonals \n",
    "    # for high-accuracy models better\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f54b6f28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch            : 1.10.1\n",
      "matplotlib       : 3.3.4\n",
      "sys              : 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:59:51) \n",
      "[GCC 9.4.0]\n",
      "pandas           : 1.4.1\n",
      "torchvision      : 0.11.2\n",
      "torchmetrics     : 0.6.2\n",
      "numpy            : 1.22.0\n",
      "pytorch_lightning: 1.5.1\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 24;\n",
       "                var nbb_unformatted_code = \"%watermark --iversions\";\n",
       "                var nbb_formatted_code = \"%watermark --iversions\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%watermark --iversions"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
